Influencer marketing: unfair commercial practices from the perspective of advertisement disclosure regulations
Bibliographic record
Abstract
The problem under analysis of this research is to discover what regulation of deceptive commercial practices are present in the field of the influencer marketing as social media influencers don’t always comply with the need for advertisement disclosure that is needed to protect the consumers. The main objective of this research is to analyse laws, regulations and cases that address unfair commercial practices in the field of influencer marketing from Italy, Germany, Nordic countries, Belgium, France, Switzerland, United Kingdom, United States, Canada, Australia, and United Arabian Emirates. The results of the research reveal various ways of how advertisement disclosure in the field of influencer marketing can be regulated and provides recommendations for the improvement of this field in Latvia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".